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Barend Mons - Open Science ≠ Open Access Articles

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Open Science is a movement to make scientific research, its data and dissemination accessible to all levels of society. This movement considers aspects such as Open Access, Open Data, Reproducible Research and Open Software.

Each of these aspects presents discreteness that need to be evaluated and discussed by the scientific community so that guidelines are established that facilitate the dissemination of scientific information.

The great challenge is to establish effective and efficient practices that allow journals to add these demands in their editorial processes, so as not only to allow data, software and methods to be accessible, but also to encourage the community to do so.

Considering these questions, this panel has as a proposal to discuss important aspects about the advancement of research communication. Some of these aspects are placed in the SciELO indexing criteria, as is the case of referencing research materials in favor of transparency and reproducibility.

Syllabus
FAIR criteria, concepts and implementation; challenges for the publication of data and methods; institutional policies for open data; adoption of TOP guidelines (Transparency and Openness Promotion); software repositories; thematic areas data repositories.

Published in: Science
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Barend Mons - Open Science ≠ Open Access Articles

  1. 1. Open Science = Open Access Articles
  2. 2. explorative reading is ‘over’, we need social machines How do metabolic syndrome, diabetes, and e.o Alzheimer relate? >>> 5 genes/metabolites thousands of papers & databases
  3. 3. Open Access for Machines
  4. 4. 727 citations today
  5. 5. http://ec.europa.eu/research/openscience/index.cfm?pg=open-science-cloud
  6. 6. G1: Aim at the lightest possible, internationally effective governance. G2: Guidance only where guidance is due. G3: Define Rules of Engagement for formal participation in the EOSC. G4: Federate the Gems across Member States. Governance recommendations of the HLEG EOSC (IFDS) GO FAIR will obviously also honour the P and I recommendations of the HLEG
  7. 7. Internet for people FAIR/FACT principles principles FAIR FAIRification services security encryption services ESFRI/clusters DNA e-Infrastrcutures increasing freedom to operate increasing freedom to operate Internet of FAIR data and Services
  8. 8. The Internet of FAIR data and Services
  9. 9. 1: Age factor….Reward only
  10. 10. 2: Ignore complexity and existing data
  11. 11. 3: Disrespect other disciplines
  12. 12. 4: publish data without a supplementary paper
  13. 13. 5: create a nightmare for machines 3
  14. 14. 6: refuse to invest in research -infrastructure
  15. 15. 7: Create Data without a Data Stewardship plan
  16. 16. C2CAMP metabolomics rare diseases chemistry AGU Sea Data Cloud Biodiversity (DISSCO) Agri/Nut GODAN Vaccines (VISI) Pers. Health Train OPEDAS FAIRwizard Training metrology CBS-types Nano (materials) ASTRON NOMAD GERDI Annotate ∂∂ ∂distributed learning 20 Implementation Networks active or in the making and growing
  17. 17. EU Countries where GO FAIR is under discussion
  18. 18. Global picture of Countries where GO FAIR is under disscussion
  19. 19. ISCO

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